{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Prepare the Functions\n",
    "exec(open('Step_3_WealthIncomeDistribution.py').read())\n",
    "Var_Income = 'Income_Market'\n",
    "DS[Var_Income] = DS[Var_Income].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Fun_UnitStat(Var):\n",
    "    # Var = 'NetWorth_Liquid'\n",
    "\n",
    "    ## by Quantiles\n",
    "    DistbyQuant = Dist_byQuantile(DS,Var,'StatWeight',list(np.linspace(0.01,0.99,num=99)))\n",
    "    TempData = pd.DataFrame({'AccQW_up':[1,0],'AccQQ_up':[1,0], \\\n",
    "                             'QQ':[DS[Var].min(),DS[Var].max()],'Quant':[0,1], \\\n",
    "                             'AccQW_down':[0,1,],'AccQQ_down':[0,1]})\n",
    "    DistByQuant = DistbyQuant.append(TempData).sort_values(by='Quant').reset_index(drop=True)\n",
    "    \n",
    "    ## by Histogram Grids\n",
    "    HistList = [*list(np.linspace(-3*1e4,0-1e3,4)),*list(np.linspace(1e3,700*1e4,36))]\n",
    "    DistByHist = Dist_byHistogram(DS,Var,'StatWeight',bins=50)\n",
    "\n",
    "    ## Summary Statistics\n",
    "    QuantFun = interp1d(DistByQuant['AccQW_down'],DistByQuant['QQ'])\n",
    "    AccQWFun = interp1d(DistByQuant['QQ'],DistByQuant['AccQW_down'])\n",
    "    QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "    SumStat = pd.Series(QuantFun(QuantList),index=[str(round(xx*100))+'%' for xx in QuantList])\n",
    "    SumStat['Min'] = DS[Var].min()\n",
    "    SumStat['Max'] = DS[Var].max()\n",
    "    SumStat['Mean'] = QW_Mean(DS,Var,'StatWeight')\n",
    "    SumStat['Gini'] = Dist_GiniCoef(DS,Var,'StatWeight')\n",
    "    \n",
    "    return {'SumStat':SumStat, 'DistByQuant': DistByQuant, 'DistByHist': DistByHist, 'QuantFun': QuantFun, 'AccQWFun': AccQWFun}\n",
    "\n",
    "def Fun_UnitStat_2(Var):\n",
    "    # Var = 'NetWorth_Liquid'\n",
    "\n",
    "    ## by Quantiles\n",
    "    DistbyQuant = Dist_byQuantile(DS,Var,'StatWeight',list(np.linspace(0.01,0.99,num=99)))\n",
    "    TempData = pd.DataFrame({'AccQW_up':[1,0],'AccQQ_up':[1,0], \\\n",
    "                             'QQ':[DS[Var].min(),DS[Var].max()],'Quant':[0,1], \\\n",
    "                             'AccQW_down':[0,1,],'AccQQ_down':[0,1]})\n",
    "    DistByQuant = DistbyQuant.append(TempData).sort_values(by='Quant').reset_index(drop=True)\n",
    "\n",
    "    ## Summary Statistics\n",
    "    QuantFun = interp1d(DistByQuant['AccQW_down'],DistByQuant['QQ'])\n",
    "    AccQWFun = interp1d(DistByQuant['QQ'],DistByQuant['AccQW_down'])\n",
    "    QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "    SumStat = pd.Series(QuantFun(QuantList),index=[str(round(xx*100))+'%' for xx in QuantList])\n",
    "    SumStat['Min'] = DS[Var].min()\n",
    "    SumStat['Max'] = DS[Var].max()\n",
    "    SumStat['Mean'] = QW_Mean(DS,Var,'StatWeight')\n",
    "    SumStat['Gini'] = Dist_GiniCoef(DS,Var,'StatWeight')\n",
    "    \n",
    "    return {'SumStat':SumStat, 'DistByQuant': DistByQuant, 'QuantFun': QuantFun, 'AccQWFun': AccQWFun}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Distribution of Income\n",
    "Stat = {}\n",
    "Stat['Income'] = Fun_UnitStat('Income_Market')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%      -7.130131e-53\n",
       "5%     -4.909825e-145\n",
       "10%      1.199061e+03\n",
       "25%      1.951844e+04\n",
       "50%      5.251388e+04\n",
       "75%      1.000000e+05\n",
       "90%      1.600059e+05\n",
       "95%      2.000163e+05\n",
       "99%      3.651454e+05\n",
       "Min     -3.400000e+04\n",
       "Max      2.600000e+06\n",
       "Mean     7.376022e+04\n",
       "Gini     4.732718e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Income']['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Negative Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.03997255)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Income']['AccQWFun'](0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-2e07d590943d>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['QQ_mid'] = TempDS['QQ_mid'].round()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QQ_mid'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "plt.plot(Stat['Income']['DistByQuant']['Quant'],Stat['Income']['DistByQuant'][['Quant','AccQQ_down']])\n",
    "plt.hlines(0,0,1,colors='k',linestyles='solid',alpha=0.2)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "TempDS = Stat['Income']['DistByHist']\n",
    "TempDS = TempDS[TempDS['QW']>1e-3]\n",
    "TempDS['QQ_mid'] = TempDS['QQ_mid'].round()\n",
    "TempDS.plot(ax=ax,kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Net Wealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Var = 'NetWorth'\n",
    "Stat[Var] = Fun_UnitStat('NetWorth_G')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%     -3.111663e+04\n",
       "5%     -8.897030e+02\n",
       "10%     2.661711e+03\n",
       "25%     4.328245e+04\n",
       "50%     3.021931e+05\n",
       "75%     8.594532e+05\n",
       "90%     1.671235e+06\n",
       "95%     2.384727e+06\n",
       "99%     5.185530e+06\n",
       "Min    -1.110000e+06\n",
       "Max     2.728450e+07\n",
       "Mean    6.762201e+05\n",
       "Gini    3.354429e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Negative Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.05777711)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['AccQWFun'](0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-ca3e317c01ef>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QQ_mid'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "plt.plot(Stat[Var]['DistByQuant']['Quant'],Stat[Var]['DistByQuant'][['Quant','AccQQ_down']])\n",
    "plt.hlines(0,0,1,colors='k',linestyles='solid',alpha=0.2)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "TempDS = Stat[Var]['DistByHist']\n",
    "TempDS = TempDS[TempDS['QW']>1e-3]\n",
    "TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n",
    "TempDS.plot(ax=ax,kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Liquid Wealth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Var = 'NetWorth_Liquid'\n",
    "Stat[Var] = Fun_UnitStat('NetWorth_Liquid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%     -2.970903e+04\n",
       "5%     -9.731222e+03\n",
       "10%    -4.097595e+03\n",
       "25%     2.181746e+01\n",
       "50%     4.002108e+03\n",
       "75%     2.200749e+04\n",
       "90%     1.151471e+05\n",
       "95%     2.893814e+05\n",
       "99%     1.183049e+06\n",
       "Min    -1.090500e+05\n",
       "Max     1.850000e+07\n",
       "Mean    6.680452e+04\n",
       "Gini    5.993683e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Negative Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.21864878)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['AccQWFun'](-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02496598737640382"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['AccQWFun'](10)-Stat[Var]['AccQWFun'](-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-ca3e317c01ef>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QQ_mid'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "plt.plot(Stat[Var]['DistByQuant']['Quant'],Stat[Var]['DistByQuant'][['Quant','AccQQ_down']])\n",
    "plt.hlines(0,0,1,colors='k',linestyles='solid',alpha=0.2)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "TempDS = Stat[Var]['DistByHist']\n",
    "TempDS = TempDS[TempDS['QW']>1e-3]\n",
    "TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n",
    "TempDS.plot(ax=ax,kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.21001756)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['NetWorth_Liquid']['AccQWFun'](1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wealth-Income Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['Ratio_NetWorthLiquid_Income_1'] = DS['NetWorth_Liquid']/DS['Income_Market']*4\n",
    "DS['Ratio_NetWorthLiquid_Income_2'] = DS['NetWorth_Liquid']/QW_Mean(DS,'Income_Market','StatWeight')*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1'] = Fun_UnitStat_2('Ratio_NetWorthLiquid_Income_1')\n",
    "Stat['Ratio_NetWorthLiquid_Income_2'] = Fun_UnitStat_2('Ratio_NetWorthLiquid_Income_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00956819, 2.14279849, 0.35245587])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['QuantFun']([0.25,0.75,0.50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['SumStat', 'DistByQuant', 'QuantFun', 'AccQWFun'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%     -0.915799\n",
       "5%     -0.208010\n",
       "10%    -0.082936\n",
       "25%     0.002392\n",
       "50%     0.088114\n",
       "75%     0.535700\n",
       "90%     2.913910\n",
       "95%     8.124397\n",
       "99%          inf\n",
       "Min         -inf\n",
       "Max          inf\n",
       "Mean    2.175976\n",
       "Gini   -0.123910\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model vs. Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wealth-to-income ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io \n",
    "import Graphics as MyGR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempMat = scipy.io.loadmat(\"../Data/WealthIncomeDist.mat\")['TempDS']\n",
    "WI_Model = pd.DataFrame(TempMat, columns=['WI', 'Frac', 'Wealth', 'Income'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TempMat = scipy.io.loadmat(\"../Data/WealthIncomeRatioDist.mat\")['TempDS']\n",
    "# WI_Model = pd.DataFrame(TempMat, columns=['WI', 'Frac'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "WI_Data = DS.loc[:, ['NetWorth_Liquid', 'Income_Market', 'StatWeight']]\n",
    "WI_Data['Prob'] = WI_Data['StatWeight']/WI_Data['StatWeight'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "MeanIncome_Quarter = (WI_Data['Income_Market']*WI_Data['Prob']).sum()/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "WI_Data['Wealth_Income_Ratio'] = WI_Data['NetWorth_Liquid']/WI_Data['Income_Market']*4\n",
    "WI_Data['Wealth_Normalized'] = WI_Data['NetWorth_Liquid']/MeanIncome_Quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 10.0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = WI_Data.sort_values('Wealth_Normalized')\n",
    "plt.plot(temp['Wealth_Normalized'], temp['Prob'].cumsum())\n",
    "plt.xlim(-1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, 10.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = WI_Model.sort_values('Wealth')\n",
    "plt.plot(temp['Wealth'], temp['Frac'].cumsum())\n",
    "plt.xlim(-1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bins = [-np.Inf]+list(np.linspace(0.05, 10, num=11))+[np.Inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "WI_Data['WI_Group'] = pd.cut(WI_Data['Wealth_Income_Ratio'], bins=Bins)\n",
    "WI_Data['Obs'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WI_Group\n",
      "(-inf, 0.05]     0.277378\n",
      "(0.05, 1.045]    0.332071\n",
      "(1.045, 2.04]    0.074706\n",
      "(2.04, 3.035]    0.038503\n",
      "(3.035, 4.03]    0.024556\n",
      "(4.03, 5.025]    0.018494\n",
      "(5.025, 6.02]    0.016528\n",
      "(6.02, 7.015]    0.010822\n",
      "(7.015, 8.01]    0.008691\n",
      "(8.01, 9.005]    0.007903\n",
      "(9.005, 10.0]    0.009311\n",
      "(10.0, inf]      0.156761\n",
      "Name: Prob, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Frac_Data = WI_Data.groupby('WI_Group')['Prob'].sum()\n",
    "print(Frac_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WI_Group\n",
      "(-inf, 0.05]     4.486145e-01\n",
      "(0.05, 1.045]    2.609484e-01\n",
      "(1.045, 2.04]    1.172257e-01\n",
      "(2.04, 3.035]    3.804590e-02\n",
      "(3.035, 4.03]    3.196834e-02\n",
      "(4.03, 5.025]    3.699046e-02\n",
      "(5.025, 6.02]    9.341335e-04\n",
      "(6.02, 7.015]    1.130506e-02\n",
      "(7.015, 8.01]    8.758018e-03\n",
      "(8.01, 9.005]    6.904878e-03\n",
      "(9.005, 10.0]    2.016945e-14\n",
      "(10.0, inf]      3.830269e-02\n",
      "Name: Frac, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "WI_Model['WI_Group'] = pd.cut(WI_Model['WI'], bins=Bins)\n",
    "Frac_Model = WI_Model.groupby('WI_Group')['Frac'].sum().sort_index()\n",
    "print(Frac_Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "YTickLable = [\"$\\leq 0$\", \"(0,1]\", \"(1,2]\", \"(2,3]\", \"(3,4]\", \"(4,5]\", \"(5,6]\", \"(6,7]\", \"(7,8]\", \"(8,9]\", \"(9,10]\", \"$>10$\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FigSize = (1/3,1/4)\n",
    "FontSize_1 = 6\n",
    "FontSize_2 = 4\n",
    "OutputFolder = 'TableGraph/'\n",
    "\n",
    "CutoffShare = 0.10\n",
    "## Histogram (Distribution of Wealth-income ratio)\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "\n",
    "ax.bar(range(len(Frac_Data.index)), Frac_Data*100,align='edge',width=-0.45,color=MyGR.MyColor('Blue',1), label='Data')\n",
    "ax.bar(range(len(Frac_Model.index)), Frac_Model*100,align='edge',width=0.45,color=MyGR.MyColor('Red',1), label='Model')\n",
    "\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "ax.set_xticks(range(len(Frac_Model.index)))\n",
    "ax.set_xticklabels(YTickLable, rotation = 90)\n",
    "plt.ylabel('Fraction of All Households (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Wealth-income ratio',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='best',frameon=False)\n",
    "\n",
    "# ax.vlines(CutoffShare*100,ax.get_ylim()[0],ax.get_ylim()[1], color=MyGR.MyColor('Black'),linestyles='dashed', linewidth=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "ax.set_rasterized(True)\n",
    "# plt.savefig(OutputFolder+'WealthIncomeRatioDist_ModelVsData.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wealth level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bins = [-np.Inf]+list(np.linspace(0.05, 10, num=11))+[np.Inf]\n",
    "YTickLable = [\"$\\leq 0$\", \"(0,1]\", \"(1,2]\", \"(2,3]\", \"(3,4]\", \"(4,5]\", \"(5,6]\", \"(6,7]\", \"(7,8]\", \"(8,9]\", \"(9,10]\", \"$>10$\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "WI_Data['W_Group'] = pd.cut(WI_Data['Wealth_Normalized'], bins=Bins)\n",
    "WI_Model['W_Group'] = pd.cut(WI_Model['Wealth'], bins=Bins)\n",
    "\n",
    "Frac_Data = WI_Data.groupby('W_Group')['Prob'].sum().sort_index()\n",
    "Frac_Model = WI_Model.groupby('W_Group')['Frac'].sum().sort_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FigSize = (1/3,1/4)\n",
    "FontSize_1 = 6\n",
    "FontSize_2 = 4\n",
    "OutputFolder = 'TableGraph/'\n",
    "\n",
    "CutoffShare = 0.10\n",
    "## Histogram (Distribution of Wealth-income ratio)\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "\n",
    "ax.bar(range(len(Frac_Data.index)), Frac_Data*100,align='edge',width=-0.45,color=MyGR.MyColor('Blue',1), label='Data')\n",
    "ax.bar(range(len(Frac_Model.index)), Frac_Model*100,align='edge',width=0.45,color=MyGR.MyColor('Red',1), label='Model')\n",
    "\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "ax.set_xticks(range(len(Frac_Model.index)))\n",
    "ax.set_xticklabels(YTickLable, rotation = 90)\n",
    "plt.ylabel('Fraction of All Households (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Wealth, normalized by the average income',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='best',frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "ax.set_rasterized(True)\n",
    "\n",
    "# plt.savefig(OutputFolder+'WealthDist_ModelVsData.eps', format='eps')\n",
    "plt.savefig(OutputFolder+'WealthDist_ModelVsData.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantiles of Liquid Net Wealth-Income Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00239205, 0.53569962, 0.08811397])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['QuantFun']([0.25,0.75,0.50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.40052491])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['AccQWFun']([1/12/2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.46714815])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_2']['AccQWFun']([1/12/2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%     -0.915799\n",
       "5%     -0.208010\n",
       "10%    -0.082936\n",
       "25%     0.002392\n",
       "50%     0.088114\n",
       "75%     0.535700\n",
       "90%     2.913910\n",
       "95%     8.124397\n",
       "99%          inf\n",
       "Min         -inf\n",
       "Max          inf\n",
       "Mean    2.175976\n",
       "Gini   -0.123910\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['SumStat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.175975824625618"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,'Ratio_NetWorthLiquid_Income_1','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Var_Wealth' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-ad9f96a1625b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mQW_Mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDS\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mVar_Wealth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'StatWeight'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mQW_Mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDS\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mVar_Income\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'StatWeight'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'Var_Wealth' is not defined"
     ]
    }
   ],
   "source": [
    "QW_Mean(DS,Var_Wealth,'StatWeight')/QW_Mean(DS,Var_Income,'StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liquid Net Worth - Income Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratio between Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905698471819156"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,'NetWorth_Liquid','StatWeight')/QW_Mean(DS,'Income_Market','StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratio between Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07621047898292432"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['NetWorth_Liquid']['QuantFun'](0.5)/Stat['Income']['QuantFun'](0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['QuantFun'](0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_2']['QuantFun'](0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fraction of HtM Households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['Flag_HtM_1'] = DS['Ratio_NetWorthLiquid_Income_1']<1/12/2\n",
    "QW_Mean(DS,'Flag_HtM_1','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['Flag_HtM_2'] = DS['Ratio_NetWorthLiquid_Income_2']<1/12/2\n",
    "QW_Mean(DS,'Flag_HtM_2','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['Flag_HtM'] = (DS['NetWorth_Liquid']<DS['Income_Market']/12/2)\n",
    "QW_Mean(DS,'Flag_HtM','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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